data_processing_testing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-12-16T21:47:57.385373Z",
     "start_time": "2024-12-16T21:47:46.410046Z"
    }
   },
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"scrapped_data/carrefour-online-sales/versions/1/Carrefour.csv\", encoding=\"latin1\")  #encoding error with UTF-8@\n",
    "df2 = pd.read_excel(\"scrapped_data/Amazon-Dataset.xlsx\")\n",
    "df3 = pd.read_csv(\"scrapped_data/e-commerce-target-sales-dataset/versions/1/orders.csv\")\n",
    "df3"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                               order_id                       customer_id  \\\n",
       "0      e481f51cbdc54678b7cc49136f2d6af7  9ef432eb6251297304e76186b10a928d   \n",
       "1      53cdb2fc8bc7dce0b6741e2150273451  b0830fb4747a6c6d20dea0b8c802d7ef   \n",
       "2      47770eb9100c2d0c44946d9cf07ec65d  41ce2a54c0b03bf3443c3d931a367089   \n",
       "3      949d5b44dbf5de918fe9c16f97b45f8a  f88197465ea7920adcdbec7375364d82   \n",
       "4      ad21c59c0840e6cb83a9ceb5573f8159  8ab97904e6daea8866dbdbc4fb7aad2c   \n",
       "...                                 ...                               ...   \n",
       "99436  9c5dedf39a927c1b2549525ed64a053c  39bd1228ee8140590ac3aca26f2dfe00   \n",
       "99437  63943bddc261676b46f01ca7ac2f7bd8  1fca14ff2861355f6e5f14306ff977a7   \n",
       "99438  83c1379a015df1e13d02aae0204711ab  1aa71eb042121263aafbe80c1b562c9c   \n",
       "99439  11c177c8e97725db2631073c19f07b62  b331b74b18dc79bcdf6532d51e1637c1   \n",
       "99440  66dea50a8b16d9b4dee7af250b4be1a5  edb027a75a1449115f6b43211ae02a24   \n",
       "\n",
       "      order_status order_purchase_timestamp    order_approved_at  \\\n",
       "0        delivered      2017-10-02 10:56:33  2017-10-02 11:07:15   \n",
       "1        delivered      2018-07-24 20:41:37  2018-07-26 03:24:27   \n",
       "2        delivered      2018-08-08 08:38:49  2018-08-08 08:55:23   \n",
       "3        delivered      2017-11-18 19:28:06  2017-11-18 19:45:59   \n",
       "4        delivered      2018-02-13 21:18:39  2018-02-13 22:20:29   \n",
       "...            ...                      ...                  ...   \n",
       "99436    delivered      2017-03-09 09:54:05  2017-03-09 09:54:05   \n",
       "99437    delivered      2018-02-06 12:58:58  2018-02-06 13:10:37   \n",
       "99438    delivered      2017-08-27 14:46:43  2017-08-27 15:04:16   \n",
       "99439    delivered      2018-01-08 21:28:27  2018-01-08 21:36:21   \n",
       "99440    delivered      2018-03-08 20:57:30  2018-03-09 11:20:28   \n",
       "\n",
       "      order_delivered_carrier_date order_delivered_customer_date  \\\n",
       "0              2017-10-04 19:55:00           2017-10-10 21:25:13   \n",
       "1              2018-07-26 14:31:00           2018-08-07 15:27:45   \n",
       "2              2018-08-08 13:50:00           2018-08-17 18:06:29   \n",
       "3              2017-11-22 13:39:59           2017-12-02 00:28:42   \n",
       "4              2018-02-14 19:46:34           2018-02-16 18:17:02   \n",
       "...                            ...                           ...   \n",
       "99436          2017-03-10 11:18:03           2017-03-17 15:08:01   \n",
       "99437          2018-02-07 23:22:42           2018-02-28 17:37:56   \n",
       "99438          2017-08-28 20:52:26           2017-09-21 11:24:17   \n",
       "99439          2018-01-12 15:35:03           2018-01-25 23:32:54   \n",
       "99440          2018-03-09 22:11:59           2018-03-16 13:08:30   \n",
       "\n",
       "      order_estimated_delivery_date  \n",
       "0               2017-10-18 00:00:00  \n",
       "1               2018-08-13 00:00:00  \n",
       "2               2018-09-04 00:00:00  \n",
       "3               2017-12-15 00:00:00  \n",
       "4               2018-02-26 00:00:00  \n",
       "...                             ...  \n",
       "99436           2017-03-28 00:00:00  \n",
       "99437           2018-03-02 00:00:00  \n",
       "99438           2017-09-27 00:00:00  \n",
       "99439           2018-02-15 00:00:00  \n",
       "99440           2018-04-03 00:00:00  \n",
       "\n",
       "[99441 rows x 8 columns]"
      ],
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>order_status</th>\n",
       "      <th>order_purchase_timestamp</th>\n",
       "      <th>order_approved_at</th>\n",
       "      <th>order_delivered_carrier_date</th>\n",
       "      <th>order_delivered_customer_date</th>\n",
       "      <th>order_estimated_delivery_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>e481f51cbdc54678b7cc49136f2d6af7</td>\n",
       "      <td>9ef432eb6251297304e76186b10a928d</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-10-02 10:56:33</td>\n",
       "      <td>2017-10-02 11:07:15</td>\n",
       "      <td>2017-10-04 19:55:00</td>\n",
       "      <td>2017-10-10 21:25:13</td>\n",
       "      <td>2017-10-18 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53cdb2fc8bc7dce0b6741e2150273451</td>\n",
       "      <td>b0830fb4747a6c6d20dea0b8c802d7ef</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-07-24 20:41:37</td>\n",
       "      <td>2018-07-26 03:24:27</td>\n",
       "      <td>2018-07-26 14:31:00</td>\n",
       "      <td>2018-08-07 15:27:45</td>\n",
       "      <td>2018-08-13 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47770eb9100c2d0c44946d9cf07ec65d</td>\n",
       "      <td>41ce2a54c0b03bf3443c3d931a367089</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-08-08 08:38:49</td>\n",
       "      <td>2018-08-08 08:55:23</td>\n",
       "      <td>2018-08-08 13:50:00</td>\n",
       "      <td>2018-08-17 18:06:29</td>\n",
       "      <td>2018-09-04 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>949d5b44dbf5de918fe9c16f97b45f8a</td>\n",
       "      <td>f88197465ea7920adcdbec7375364d82</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-11-18 19:28:06</td>\n",
       "      <td>2017-11-18 19:45:59</td>\n",
       "      <td>2017-11-22 13:39:59</td>\n",
       "      <td>2017-12-02 00:28:42</td>\n",
       "      <td>2017-12-15 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ad21c59c0840e6cb83a9ceb5573f8159</td>\n",
       "      <td>8ab97904e6daea8866dbdbc4fb7aad2c</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-02-13 21:18:39</td>\n",
       "      <td>2018-02-13 22:20:29</td>\n",
       "      <td>2018-02-14 19:46:34</td>\n",
       "      <td>2018-02-16 18:17:02</td>\n",
       "      <td>2018-02-26 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99436</th>\n",
       "      <td>9c5dedf39a927c1b2549525ed64a053c</td>\n",
       "      <td>39bd1228ee8140590ac3aca26f2dfe00</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-03-09 09:54:05</td>\n",
       "      <td>2017-03-09 09:54:05</td>\n",
       "      <td>2017-03-10 11:18:03</td>\n",
       "      <td>2017-03-17 15:08:01</td>\n",
       "      <td>2017-03-28 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99437</th>\n",
       "      <td>63943bddc261676b46f01ca7ac2f7bd8</td>\n",
       "      <td>1fca14ff2861355f6e5f14306ff977a7</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-02-06 12:58:58</td>\n",
       "      <td>2018-02-06 13:10:37</td>\n",
       "      <td>2018-02-07 23:22:42</td>\n",
       "      <td>2018-02-28 17:37:56</td>\n",
       "      <td>2018-03-02 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99438</th>\n",
       "      <td>83c1379a015df1e13d02aae0204711ab</td>\n",
       "      <td>1aa71eb042121263aafbe80c1b562c9c</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-08-27 14:46:43</td>\n",
       "      <td>2017-08-27 15:04:16</td>\n",
       "      <td>2017-08-28 20:52:26</td>\n",
       "      <td>2017-09-21 11:24:17</td>\n",
       "      <td>2017-09-27 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99439</th>\n",
       "      <td>11c177c8e97725db2631073c19f07b62</td>\n",
       "      <td>b331b74b18dc79bcdf6532d51e1637c1</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-01-08 21:28:27</td>\n",
       "      <td>2018-01-08 21:36:21</td>\n",
       "      <td>2018-01-12 15:35:03</td>\n",
       "      <td>2018-01-25 23:32:54</td>\n",
       "      <td>2018-02-15 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99440</th>\n",
       "      <td>66dea50a8b16d9b4dee7af250b4be1a5</td>\n",
       "      <td>edb027a75a1449115f6b43211ae02a24</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-03-08 20:57:30</td>\n",
       "      <td>2018-03-09 11:20:28</td>\n",
       "      <td>2018-03-09 22:11:59</td>\n",
       "      <td>2018-03-16 13:08:30</td>\n",
       "      <td>2018-04-03 00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99441 rows × 8 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 12
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-16T21:22:14.652925Z",
     "start_time": "2024-12-16T21:22:14.647734Z"
    }
   },
   "cell_type": "code",
   "source": "print(df.columns)",
   "id": "701e20b5b3c9ca82",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['order_id', 'order_date', 'status', 'item_id', 'sku', 'qty_ordered',\n",
      "       'price', 'value', 'discount_amount', 'Discount_Percent',\n",
      "       'Selling price', 'category', 'payment_method', 'year', 'month',\n",
      "       'Gender', 'age', 'City', 'Reasons for cancelation and order refund'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-16T21:20:36.652273Z",
     "start_time": "2024-12-16T21:20:33.184863Z"
    }
   },
   "cell_type": "code",
   "source": "df.sketch.ask(f\"tell me what are the most important columns of the dataframe{df.columns}\")",
   "id": "756086b6c0ca7451",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ],
      "text/html": [
       "\n",
       "The most important columns of the dataframe are likely the ones that provide information about the orders, such as order_id, order_date, status, item_id, sku, qty_ordered, price, value, discount_amount, Discount_Percent, Selling price, category, payment_method, year, month, Gender, age, City, and Reasons for cancelation and order refund. These columns contain key information about the orders and can be used to analyze and understand the data."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 7
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "7d7f12a18442e33"
  }
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